The growing global demand for animal protein, particularly chicken meat, challenges poultry farming to adapt production systems through the adoption of digital technologies. Among the promising advances in artificial intelligence (AI), large language models (LLMs) hold potential to enhance decision-making in broiler production by supporting environmental control through the interpretation of climatic data, the generation of reports to optimize conditions, guidance on ventilation adjustments, recommendations for thermal management, assistance in air quality monitoring, and the translation of simulation results into actionable suggestions to improve bird welfare. For this purpose, the key limitations of LLMs in terms of transparency, accuracy, precision, and relevance must be effectively addressed. This study investigates the impact of retrieval-augmented generation (RAG) on improving LLM precision and relevance for environmental control in broiler production. Experiments with the OpenAI GPT-4o model and semantic similarity analysis were used to evaluate response quality with and without RAG. The results confirmed the approach’s effectiveness while identifying areas for improvement. A paired t-test revealed significantly higher similarity scores with RAG, demonstrating its impact on response quality. This study contributes to the field by advancing RAG-enhanced LLMs for environmental control, addressing market demands by demonstrating how AI improves decision-making for productivity and animal welfare, and benefits society by providing small-scale producers with cost-effective and accessible solutions for actionable insights.

Driven by the global rise in animal protein demand, poultry farming has evolved into a highly intensive and technically complex sector. According to the FAO, animal protein production increased by about 16% in the past decade, with poultry alone expanding by 27% and becoming the leading source of animal protein. This intensification requires rapid, complex decisions across multiple aspects of production under uncertainty and strict time constraints. This study presents the development and evaluation of a conversational decision support system (DSS) designed to support decision-making to assist poultry producers, particularly broiler producers, in addressing technical queries across five key domains: environmental control, nutrition, health, husbandry, and animal welfare. As a proof-of-concept study, the reference context is intensive broiler production, covering common floor-rearing housing settings, including environmentally controlled and mechanically ventilated houses. The system combines a large language model (LLM) with retrieval-based generation (RAG) to ground responses in a curated corpus of scientific and technical literature. Additionally, it adds a reasoning component using Paraconsistent Annotated Evidential Logic Eτ, a non-classical logic designed to handle contradictory or incomplete information. Methodologically, Logic Eτ is used as a workflow-level control mechanism to gate clarification, domain routing, and answer adequacy signaling, rather than serving only as a post hoc label on generated outputs. Evaluation was conducted by comparing system responses with expert reference answers using semantic similarity (cosine similarity with SBERT embeddings). The results indicate that the system successfully retrieves and composes relevant content, while the paraconsistent inference layer makes results easier to interpret and more reliable in the presence of conflicting or insufficient evidence. These findings suggest that the proposed architecture provides a viable foundation for explainable and reliable decision support in modern poultry production, achieving consistent reasoning under contradictory or incomplete information where conventional RAG chatbots may produce unstable guidance.